Metabolite signature of human malignant thyroid tissue: A systematic review and meta‐analysis

Abstract Background Thyroid cancer (TC) is the predominant malignancy within the endocrine system. However, the standard method for TC diagnosis lacks the capability to identify the pathological condition of all thyroid lesions. The metabolomics approach has the potential to manage this problem by identifying differential metabolites. Aims This study conducted a systematic review and meta‐analysis of the NMR‐based metabolomics studies in order to identify significant altered metabolites associated with TC. Methods A systematic search of published literature in any language in three databases including Embase, PubMed, and Scopus was conducted. Out of 353 primary articles, 12 studies met the criteria for inclusion in the systematic review. Among these, five reports belonging to three articles were eligible for meta‐analysis. The correlation coefficient of the orthogonal partial least squares discriminant analysis, a popular model in the multivariate statistical analysis of metabolomic data, was chosen for meta‐analysis. The altered metabolites were chosen based on the fact that they had been found in at least three studies. Results In total, 49 compounds were identified, 40 of which were metabolites. The increased metabolites in thyroid lesions compared normal samples included lactate, taurine, alanine, glutamic acid, glutamine, leucine, lysine, phenylalanine, serine, tyrosine, valine, choline, glycine, and isoleucine. Lipids were the decreased compounds in thyroid lesions. Lactate and alanine were increased in malignant versus benign thyroid lesions, while, myo‐inositol, scyllo‐inositol, citrate, choline, and phosphocholine were found to be decreased. The meta‐analysis yielded significant results for three metabolites of lactate, alanine, and citrate in malignant versus benign specimens. Discussion In this study, we provided a concise summary of 12 included metabolomic studies, making it easier for future researchers to compare their results with the prior findings. Conclusion It appears that the field of TC metabolomics will experience notable advancement, leading to the discovery of trustworthy diagnostic and prognostic biomarkers.


| INTRODUCTION
The common definition for metabolomics is the largescale analysis of small molecules, called metabolites, with a molecular weight of <1500 daltons (Da).The metabolomics approach can be used in various biological samples such as biological fluids (whole blood, serum, plasma, urine, saliva, sweat, milk, semen, etc.), tissues, cells, plants, and foods. 1,2What makes metabolomics important is that it is the final downstream omics that brings researchers very close to the molecular phenotype understanding of an organism.In this regard, it can represent the metabolic differences between health and disease and can be applied in precision medicine. 1xamining the human metabolome can open the new windows for identification of metabolites with promising applications in diagnosis, prognosis, and treatment of different diseases.In fact, metabolic profiles have the capacity to function as potential biomarkers for the early diagnosis, progression, and outcomes of different diseases, encompassing cancer.On the other hand, the metabolites identification can lead to targeted treatments that take advantage of metabolic pathways.
Due to the complexity of the metabolome, robust analytical techniques are required for quantitative analysis.The most common techniques used in metabolomics are nuclear magnetic resonance (NMR), liquid chromatography-mass spectrometry (LC-MS), and gas chromatography-mass spectrometry (GC-MS), which allow identification and quantification of metabolites. 3,4MR utilizes the magnetic characteristics of specific atomic nuclei, such as 1 H and 13 C found in molecules.By subjecting these nuclei to a powerful magnetic field and exposing them to radio frequency pulses, they absorb and subsequently emit electromagnetic radiation at distinct frequencies.As a result, a distinct identifier is generated for each metabolite present in a sample.NMR spectroscopy is an essential instrument for studying metabolomics, providing significant benefits in terms of non-invasive, quantitative, and consistent examination of metabolites within intricate biological samples.Consequently, it plays a vital role in enhancing the comprehension of metabolic pathways and their impact on diverse physiological and pathological states.Hence NMR-based metabolomics is beneficial and practical for extensive research in the field of cancer. 5,6mong all human cancers that have been studied by NMR-based metabolomics approach, the contribution of thyroid cancer studies is restricted.Meanwhile, thyroid cancer is the most common endocrine malignancy and its prevalence is increasing. 7Diagnosis of thyroid cancer is performed by the cytological examination of fine needle aspiration biopsy (FNAB) which is the most common and efficient method for preoperative diagnosis of the nature of thyroid nodules.
Thyroid FNAB examination, with 90% accuracy, is able to discriminate 70% of specimens as benign or malignant nodules.However, the important limitation of the method is that it is not able to differentiate follicular adenomas from carcinomas in 10%-30% of cases.These cases, which have an indeterminate cytology, undergo diagnostic surgery.But the rate of malignancy in this group is about 14%-20%, which shows that 80% of patients undergo unnecessary surgery. 8,9he American Thyroid Association (ATA) has suggested that researchers look for biomarkers that have the power of differential diagnosis. 10Metabolomics approach has a high potency to identify biomarkers in the context of screening and diagnosis of cancer.So, the problem of thyroid FNABs with indeterminate cytology may be managed by the metabolomics capabilities.Distinguishing metabolic alterations between cancerous and non-cancerous thyroid lesions has the potential to facilitate the creation of more precise and delicate diagnostic techniques.This could potentially decrease the occurrence of misdiagnoses and avoid unnecessary surgical procedures.Additionally, it would decrease the necessity for invasive methods and mitigate the accompanying risks.
The main objective of the current study was to systematically review studies that aimed to identify altered metabolites for discriminating between thyroid lesions using an NMR-based metabolomics approach.In this regard, we performed a systematic review and metaanalysis to answer the following two focused questions: (1) According to the NMR-based metabolomics, which metabolites are significantly higher/lower in human thyroid lesions versus normal tissues, as well as human thyroid cancer versus benign tissues?(2) Do these Conclusion: It appears that the field of TC metabolomics will experience notable advancement, leading to the discovery of trustworthy diagnostic and prognostic biomarkers.

K E Y W O R D S
meta-analysis, metabolomics, NMR, thyroid carcinoma, thyroid lesions metabolites possess the ability to differentiate malignancy from benignity?

| Data sources and search strategy
This systematic review and meta-analysis has been registered at the International Prospective Register of Systematic Reviews (PROSPERO) with the registration ID number of CRD42022370330.PROSPERO functions as a thorough and varied database committed to the advance registration of protocols for systematic reviews.The main aim of PROSPERO is to promote transparency, reduce bias, and improve the caliber of systematic reviews in diverse areas of research, particularly in healthcare, and social interventions.
We conducted a systematic search of published literature in any language in three different databases (Embase, PubMed, and Scopus) from inception to November 1, 2022, by use of the following approach.We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method for the systematic reviews.The reference lists of the selected articles were also hand-searched.

| Keywords and their connections
The following keywords with their respective connections used for references identification.Instructions on how to search each of the databases are provided in the Data S1.No exclusion criteria were applied during this step ("metabolomics" OR "metabonomics" OR "metabolome" OR "metabolic profile" OR "metabolomic profile" OR "metabolites profile") AND ("thyroid neoplasm" OR "thyroid carcinoma" OR "thyroid cancer" OR "thyroid adenoma" OR "thyroid nodule" OR "thyroid tumor" OR "nodular goiter" OR "multinodular goiter" OR "multi-nodular goiter" OR "MNG").

| Study selection and eligibility criteria
Following references identification, a meticulous screening process was initiated to ensure the inclusion of relevant studies in the analysis.Initially, all identified studies were imported into the Endnote software for systematic management.Subsequently, any duplicate publications were removed.The screening phase then commenced, where the titles, abstracts, and keywords of the remaining studies underwent thorough evaluation by two reviewers (S.Adeleh Razavi and Mehdi Hedayati).This rigorous dual-review process aimed to meticulously assess each study's alignment with the research focus on thyroid cancer and metabolomics and relevance to the study objectives.Study exclusions encompassed irrelevant studies, conference abstracts, nonoriginal papers such as reviews, editorials, and book chapters, as well as studies focusing on animal, plant, or cellular models.Furthermore, non-English studies were also omitted from the analysis.
Given the diverse range of specimen types, including serum, plasma, tissue, among others, and the various analytical platforms prevalent in metabolomics research (such as GC-MS, LC-MS, NMR), interpreting and synthesizing research findings can pose significant challenges.To ensure homogeneity in the data analyzed, stringent eligibility criteria were established for study inclusion.Specifically, the scope of the review was limited to encompass tissue and fine-needle aspiration biopsy (FNAB) specimens analyzed using the NMR analytical platform.Throughout the study selection process, any discrepancies were meticulously addressed through scientific deliberation among the research team.The flow chart of study selection is presented in Figure 1.

| Quality assessment and data extraction
The quality of the included studies was determined using the case-control version of "The Newcastle-Ottawa Scale (NOS)" for assessing the quality of non-randomized studies in meta-analyses (https:// www.ohri.ca/ progr ams/ clini cal_ epide miolo gy/ oxford.asp).The NOS evaluates the quality of the study based on the three main criteria of selection, comparability, and exposure.Selection (with four items) assesses how well the selection of cases and controls in individual studies is defined and whether they are representative of the target population.Comparability (with one item) assesses the comparability of cases and controls on important factors or characteristics.Exposure (with three items) assesses how the exposure was determined for the case and control groups.A study can be awarded a maximum of one star for each item within the selection and exposure categories.A maximum of two stars can be given for comparability.The exposure item was not applicable in this study.Therefore, only selection and comparability were evaluated.Accordingly, the highest possible score was 6 stars (Table 1).
For the eligible studies, data were extracted using data extraction form that included the following items.Authors and year of publication, country where the research was conducted, number of participants and their age, type of sample (e.g., intact tissue, tissue extract, FNAB, and etc.) and how to collect it, the method of examining the pathological condition (e.g., pre or postoperative histopathological examination, and etc.), the number and type of studied samples based on the pathology report (Table 2).Methodology information (e.g., approach, type of NMR instrument, the field strength of the NMR magnet, and etc.) was recorded in a separate form (Table 3).
For each study, the number and type of identified metabolites, their increased or decreased level, and all presented results from multivariate, and univariate analyses were recorded.Association between the level of a given metabolite with malignancy or benignity that reported through correlation coefficient, fold change, receiver operating characteristic curve, and etc. were recorded carefully and with all the details.

| Data synthesis and meta-analysis
First, a comprehensive qualitative review was conducted on all included studies (12 articles).The number and type of metabolites identified in each study were represented using frequency charts to introduce general trends.The main metabolites were then meta-analyzed based on the reported correlation coefficients in the studied groups.
It is important to mention that, because of the lack of adequate information, only five reports pertaining to three studies were incorporated in the meta-analysis.Despite concerted efforts to request supplementary data, we did not receive a response, thereby the breadth and depth of the meta-analytical investigation was limited.This limitation impeded the acquisition of additional datasets for comprehensive analysis.
Heterogeneity between studies (using Q-test and I 2 statistics) and studies effect (using Forest plots) were assessed by the Comprehensive Meta-Analysis (CMA V3) and MedCalc 19.2 software.Heterogeneity was considered statistically significant at p ≤ 0.10.The significance level of studies effect was defined by a p < 0.05.
With the aim of identifying the influencing metabolites, a bipartite network was drawn for the metabolites that were recognized in the systematic review.A bipartite network comprised of two distinct categories of nodes, wherein connections solely transpired between nodes of dissimilar categories.Within this investigation, the nodes exemplified two dissimilar categories of metabolites and studies.This form of network visualization led to the comprehension of connections between metabolites and studies.4][25] Network projection was employed to streamline bipartite networks by converting them into unipartite networks.This conversion aided in the more  efficient analysis of the relationships within the data.8] 3 | RESULTS

| Literature search results
In the systematic search of three databases (Embase, PubMed, Scopus), 353 articles were found, of which 174 were duplicates.After removing duplicates and removing non-English papers, reviews/editorials/book chapters, conference abstracts, animal/plant/cellular studies, and irrelevant studies, 42 articles remained that their full text were read.Of these, three articles were not retrieved, because they used imaging methods or capillary electrophoresis.Out of 39 articles, 19 articles used a platform other than NMR (GC-MS or LC-MS), and eight articles studied samples other than tissue/FNAB (serum, plasma, etc.).Finally, 12 articles [11][12][13][14][15][16][17][18][19][20][21][22] were included in the qualitative study.Out of 12 articles and due to the limitations of the reported results, five reports related to three articles were selected for the quantitative study (Figure 1).It should be noted that the references of 12 included studies were hand-searched and 10 articles [29][30][31][32][33][34][35][36][37][38] apparently related to the topic were assessed for eligibility.After reading the full text, it was found that most of these articles are dated prior to 2000, and none of them possess the components that are present in today's untargeted metabolomics studies.For example, most of them investigated a specific region of the NMR spectrum (e.g., a ratio of the intensities of resonances at 0.9 ppm and 1.7 ppm) 29,31 and were not among metabolites profiling studies.There were no common multivariate analyses (e.g., principal component analysis, or orthogonal partial least squares discriminant analysis [OPLS-DA]) in them either.Therefore, none of these 10 articles were recognized as eligible.

| Eligible studies characteristics
The studies included in the current study were published between 2011 and 2021 and belonged to the countries of the USA (one article), Italy (four articles), Poland (two articles), China (three articles), and Korea (two articles).The highest number of study participants were 141 patients 22 and the lowest number were 14 patients. 21Among the participants in the included studies, the minimum age was 9 years and the maximum age was 88 years. 12,13The studies used different types of samples including FNAB, tissue extract and intact tissue.The number and cyto/histopathology of evaluated specimens are presented in Table 2. Except for the Seo et al. study 20 that compared papillary thyroid carcinoma (PTC) patients with or without lymph nodes metastasis, other studies compared metabolite profiles between the thyroid lesions group and the normal group, as well as the malignant and benign groups.All studies were case-control studies and chose the metabolomic profiling approach.Seven studies used highresolution magic angle spinning 1 H NMR (HRMAS 1 H NMR) and five studies used 1 H NMR. Except for Metere et al.'s study, 21 the used pulse program of all studies was Carr-Purcell-Meiboom-Gill pulse sequence.Other methodological information, such as metabolite assignment, multivariate analysis, univariate analysis, statistical software, and pathway analysis tool is fully presented in Table 3.
The quality assurance of included studies was performed based on the NOS method.Out of 12 reviewed studies, seven articles scored 5 stars, three articles scored 4 stars, one article scored 3 stars, and one article scored 2 stars.These stars were interpreted as follows: 5 stars: good, 3-4 stars: fair, and 0-2 stars: poor.Therefore, in this study, seven articles with good quality, four articles with fair quality and one article with poor quality were included (Table 1).

| Systematic review results
Jordan and colleagues did not suggest specific metabolites in their results.They simply demonstrated, through univariate statistical analysis, that NMR-based metabolomic profiles were sensitive enough to differentiate between normal, PTC and follicular adenoma (FA) in tissue and FNAB samples. 11n another study, Seo et al. conducted a comparison between two groups of PTC patients, one with metastasis to lymph nodes and the other without, and found that there was no significant distinction in metabolic profile between the two groups.However, they proposed in this comparison, lactate was an important metabolite.In their subsequent study, Seo et al. compared PTC patients with lateral lymph nodes metastasis to those without, and similarly found no statistically remarkable difference in metabolic profiles between the groups.But they did identify lactate and myo-inositol as important metabolites in patients with lateral lymph nodes metastasis. 20n their research, Metere et al. compared 11 samples of thyroid cancer and a control group comprising of both healthy and benign samples.This study showed that acetic acid, alanine, creatine, formic acid, glutathione, isoleucine, lactate, phenylalanine, and tyrosine were higher and citrate, myo-inositol, and threonine were lower in the thyroid cancer group. 21Due to the inclusion of one of the thyroid lesions (benign tissues) in the control group, the results of this study are not incorporated in the subsequent sections.
The following sections present the results of the other studies that have clearly elucidated the differences in metabolites status among the studied groups (thyroid lesions vs. normal and malignant vs. benign thyroid tissues/ FNABs).

| Altered metabolites in thyroid lesions versus normal
The number of individual compounds identified in nine studies [12][13][14][15][16][17][18][19]22 was 49 that their common names, PubChem CID and Human Metabolome Database (HMDB) ID are listed in Table S1. Sinificant altered metabolites between thyroid lesions and normal specimens observed in included articles [12][13][14][15][16][17][18][19]22 have been summarized using the vote-counting charts.Each metabolite received one vote per article.The study conducted by Tian et al. 15 used two different types of samples (intact tissue and tissue extract) and reported separate results for each type of sample.Accordingly, in the systematic review and meta-analysis, these results have been evaluated as two separate reports under the title of Tian The metabolites that have been found to increase in thyroid lesions versus normal in at least three reports were: lactate, taurine, alanine, glutamic acid, glutamine, leucine, lysine, phenylalanine, serine, tyrosine, valine, choline, glycine, and isoleucine.The term "lipids" was found to be the group of metabolites that have decreased in thyroid lesions vs. normal in at least three studies. Figure 2shows qualitative vote-counting charts of altered metabolites in thyroid lesions versus normal specimens.The complete lists of increased/decreased metabolites for each article are provided in Tables S2  and S3.
According to the Table S2, a bipartite network was first constructed (Figure 3A), and then the bipartite network projection was created.It should be noted that the obtained graph was filtered according to the numbers of connections (edges) between nodes.This means that metabolites that were reported in more than one paper remained in the graph.Therefore, the graph is a weighted network, that is, the size of the nodes and the thickness of the edges indicate the number of times a particular metabolite has been reported (Figure 3B).This analysis was only carried out for Table S2.
Metabolites that showed an increase in thyroid lesions were used to calculate the correlation diagram (Figure 4).
This diagram shows the percentage of co-reported frequency of the two metabolites in the articles.For example, if the correlation between two metabolites is 0.5, it means that these two metabolites were found simultaneously in 50% of the included studies..3.2| Altered metabolites in malignant versus benign In at least three reports, it has been suggested that lactate and alanine were increased in malignant versus benign thyroid tissues/FNABs.On the other hand, at least three studies have indicated that myo-inositol, scyllo-inositol, citrate, choline, and phosphocholine were found to be decreased in malignant thyroid tissues/FNABs in comparison to those that were benign.Figure 5A,B shows qualitative vote-counting charts of altered metabolites in malignant versus benign thyroid specimens.The complete lists of increased/decreased metabolites for each article are provided in Tables S4  and S5. Figure 5C,D shows the correlation diagram of increased and decreased metabolites in malignancy, respectively.

| Meta-analysis results
Due to the constraints of the presented data in the articles, the meta-analysis was performed only on three metabolites (lactate, alanine, and citrate) that were recognized as malignancy biomarkers in systematic review.The meta-analysis was carried out for each metabolite on the correlation coefficients obtained from the OPLS-DA models.Positive values indicate a relatively higher metabolite level in malignant tissues/FNABs than nonmalignant (benign or normal).Five reports related to three articles were used in the meta-analysis: (a) the study by Tian et al., 15 which used intact tissue and compared malignant to benign samples (part 1 of the Tian et al. study), (b) the study by Tian et al., 15 which used tissue aqueous extract and compared malignant to benign samples (part 2 of the Tian et al. study), (c) the study by Li et al., 18 which used intact tissue and compared malignant to normal samples, (d) the study by Skorupa et al. 22 named as part 1 of the Skorupa et al., which used intact tissue and compared malignant to non-tumoral tissue (colloid goiter), and (e) the study by Skorupa et al. 22 named as part 2 of the Skorupa et al., which used intact tissue and compared malignant to non-tumoral tissue (chronic thyroiditis).
According to the meta-analysis of lactate correlations obtained from the five OPLS-DA models, a robust statistically significant of p < 0.001 for both fixed and random models with low heterogeneity among studies (Q = 6.75,I 2 = 40.74%,95% CI for I 2 = 0.00-78.14,p = 0.1497) were obtained.The total correlation coefficient for fixed effects was 0.774 (95% CI = 0.720-0.819)and the total correlation coefficient for random effects was 0.775 (95% CI = 0.695-0.835).
Alanine was the other metabolite included in the metaanalysis.A statistically significant findings for both fixed and random models (p < 0.001) with no evidence of heterogeneity (Q = 3.1715,I 2 = 0.00%, 95% CI for I 2 = 0.00-75.31,p = 0.5295) were observed.The total correlation coefficient for both fixed and random effects was 0.695 (95% CI = 0.625-0.753).

| DISCUSSION
This study conducted a systematic review and metaanalysis on NMR-based metabolomics studies that were F I G U R E 2 Increased metabolites (A) and decreased metabolites (B) in thyroid lesions versus normal specimens according a qualitative vote-counting results.Each metabolite received one vote per article.performed on thyroid tissue samples or FNABs.One of our main efforts in this review was to present altered metabolites in thyroid lesions especially thyroid cancer that has been discovered so far in research those analyze metabolites using NMR spectroscopy.Introduced metabolites were presented with PubChem CID and HMDB ID (Table S1) to facilitate the comparison of the results presented here with the future reports findings.
In the systematic review, two types of comparisons were evaluated to identify altered metabolites: comparison of thyroid lesions versus normal and comparison of malignant versus benign specimens, which recognized a total of 49 compounds.Out of these 49 compounds, nine of them were not specific metabolites and were labeled as amino acids, choline-containing compounds, lipids, Nacetyl glycoprotein signals, nuclear acid, saturated fatty  S2.Compounds with a general term that were not an identifiable metabolite were removed.acids, unknown-1, unknown-2, and unsaturated fatty acids.The remaining were 40 individual metabolites classified into nine groups: (I) amino acids (alanine, cysteine, cystine, glutamic acid, glutamine, glycine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, serine, taurine, threonine, tyrosine, and valine), (II) ganic acids (acetate, ascorbate, citrate, formate, fumarate, lactate, and succinate), (III) choline compounds (choline, glycerophosphocholine, and phosphocholine), (IV) lipoproteins (low-density lipoprotein and very-low-density lipoprotein), (V) nucleic acid derivatives (inosine, hypoxanthine, uracil, uridine, and xanthine), (VI) carbocyclic sugars (myo-inositol and scyllo-inositol), (VII) amino alcohols (ethanolamine and phosphoethanolamine), (VIII) peptides (glutathione), and (IX) ketones (acetone).Out of these 40 metabolites, 13 of them have been reported only once.The metabolites that at least three studies indicated their increase in thyroid lesions were lactate, taurine, alanine, glutamic acid, glutamine, leucine, lysine, phenylalanine, serine, tyrosine, valine, choline, glycine, and isoleucine (the metabolites have been listed in order of the highest frequency).Among these, lactate and alanine were found to have increased in thyroid cancer versus benign lesions according to five and three studies, respectively.However, the only compounds that at least three articles indicated a reduction in the thyroid lesions were lipids.On the other hand, myo-inositol and scyllo-inositol were found to be reduced in thyroid cancer by six studies, while citrate, choline, and phosphocholine were shown to be reduced by four, three, and three articles, respectively.
Calculating the relationship between two metabolites based on the percentage of their simultaneous reporting in studies showed that the pair of lactate and taurine had the highest correlation in thyroid lesions samples.Such a correlation was observed in malignant samples between the pair of increased metabolites of alanine-phenylalanine, choline-lactate, lactate-phosphocholine, and lactatetaurine.In malignant specimens, the pair of decreased myo-inositol and scyllo-inositol was reported together in 75% of included studies.Other metabolites decreased in malignancy that showed a good correlation were: citrate and myo-inositol, citrate and scyllo-inositol, myoinositol and phosphocholine, and phosphocholine and scyllo-inositol.To begin our discussion on the metabolites that have undergone the most significant changes in thyroid lesions, particularly in cases of thyroid cancer, lactate is the appropriate starting point.One of the first and most well-known metabolic changes in cancer cells is increased glucose consumption by tumor cells.It is now almost proven that tumor cells increase glucose uptake and produce large amounts of lactate even in the presence of oxygen. 39This phenomenon, known as the "Warburg effect", explains specific aspect of cancer cell metabolism.In cancer, multiple signaling pathways affect glucose metabolism.Phosphoinositide 3-kinase-protein kinase B (PI3K-AKT) signaling pathway is one of the classic pathways activated by insulin or other growth factors and stimulates glycolysis. 40AKT can increase glycolytic activity directly by phosphorylating hexokinase and indirectly by phosphorylating regulators of glucose transporters. 41,42When glucose enters the oxidative phosphorylation pathway, the greatest amount of energy is produced.But during the lack of oxygen, the end product of glycolysis is lactate.However, in the presence of oxygen, cancer cells prefer the fermentation pathway of glucose to lactate.Apparently, the conversion of pyruvate to lactate by cancer cells provides the redox cofactors required for biosynthetic functions. 43ompared with normal or benign thyroid specimens, the increase of lactate often is paralleled by alanine.This event probably occurs through the Cahill cycle, where pyruvate is metabolized to alanine via alanine aminotransferase to be used for energy production. 44Genetic alterations in metabolic enzymes can cause these abnormal accumulations of intracellular metabolites.Such an event is seen in the case of the increase of the other amino acids status in tumor cells.For example, some cancer cells amplify the enzyme regulating the serine synthesis pathway (phosphoglycerate dehydrogenase) to obtain serine for de novo synthesis of purines and thymidine. 45,46In this process, glycine is also involved alongside serine and serves as one-carbon intermediates in the biosynthesis of nucleotides, lipids, and proteins. 47,48Glutamine is also a very important amino acid in cancer cell metabolism.Despite being a non-essential amino acid, glutamine plays a crucial role in the biosynthesis of multiple compounds, including glutathione, nucleotides, fatty acids, and the other non-essential amino acids, by acting as a significant nitrogen donor. 49ince cancer cells are proliferating unbridled, they need more fatty acids to produce lipid membrane.In these cells, fatty acids are synthesized both through diet and from glucose, glutamine or acetate. 50It is possible to justify the decreased level of citrate in malignancy based on fatty acids synthesis, where citrate is transported from the mitochondria to the cytoplasm, and undergoes degradation to reduce acetyl-CoA for the synthesis of fatty acids and oxaloacetate. 15The other necessary substrates for the lipid biosynthesis are choline and choline-containing compounds.These compounds such as phosphocholine, phosphatidylcholine, and glycerophosphocholine are the main constituents of cell membranes. 51,52However, different studies have conflicting findings regarding to status of choline in thyroid nodules.In this study, choline was found to be increases in thyroid lesions, but decreased in thyroid cancer.
Inositol is a sugar alcohol and has nine stereoisomers, among which myo-inositol and scyllo-inositol are the most common. 53These two, which play an important role in maintaining cell osmolality, 54,55 showed a significant reduction in thyroid cancer compared to benign nodules. 56It seems that the decrease of these two metabolites is related to the disturbance of the osmolality balance of cancer cells.
A brief meta-analysis was performed to obtain more reliable metabolites that are altered in thyroid malignancy.Based on the meta-analysis of correlation coefficient results, lactate and alanine were upregulated and citrate was downregulated.Accordingly, lactate, alanine, and citrate are among the biomolecules that have the potential to be used as markers for thyroid cancer.Recent research has shown that when lactate shuttles are suppressed, the ability of thyroid cancer cells to proliferation and utilize glucose is significantly reduced in a low-glucose environment.Consequently, directing efforts towards hindering the glycolytic and lactate processing pathways could potentially be an effective and influential method of treating thyroid cancer. 57Another study, which focused on the diagnostic method for thyroid cancer using amino acid metabolomics in saliva, found that a combination of alanine, valine, proline, and phenylalanine can enhance the precision of early detection of thyroid cancer. 58The findings of the current study are in contrast to those of this particular study, which identified alanine reduction as a diagnostic condition.It is worth noting that our study examined tissue and FNABs samples, whereas the study being referenced focused on saliva samples.This distinction suggests that the metabolic changes observed in thyroid cancer may vary between local regions and the body as a whole.
The study conducted by Khatami et al. has identified citrate as the most significant diagnostic marker for thyroid cancer. 56The growth of different types of tumors can be inhibited by citrate, as demonstrated through multiple mechanisms.Some studies have proposed that dietary supplements which include citrate might have potential anticancer properties. 59All of these studies validate that there are significant alterations in metabolic pathways in thyroid lesions, particularly in cases of thyroid cancer.Nevertheless, given the dynamic nature of metabolic processes and their efficacy in response to both endogenous and exogenous factors, comprehensive investigations with a significant number of participants is necessary to determine metabolic biomarkers and apply them in clinical decisions.

| CONCLUSION
Collectively, this systematic review and meta-analysis offer a summary of the association between several metabolites and thyroid lesions/thyroid cancer based on NMR metabolomics.A bright future can be imagined for using the NMR approach in differentiating among the thyroid lesions pathology, particularly, malignancy from benignity.This study summarized the status of 49 compounds (40 metabolites) concerning thyroid lesions/thyroid cancer.In thyroid lesions, 38 metabolites were increased and seven metabolites were decreased.Ten metabolites showed an increase, while 12 metabolites indicated a decrease in thyroid cancer.We performed meta-analysis for three metabolites can be potential biomarkers in distinguishing malignancy from benignity according to the correlation coefficients of OPLS-DA model, detecting three significant correlation coefficients.The alteration of metabolites in thyroid lesions, particularly thyroid cancer, indicates the disruption of various metabolic pathways.Additional investigation is necessary to comprehend the fundamental metabolic pathways along with molecular mechanisms involved in thyroid cancer and convert them into clinical applications.The limited number of studies and insufficient information presented were a gap of knowledge that needs to be eliminated for future studies.If NMR-based metabolomics is well validated for pathological differentiation of thyroid nodules, using it could potentially reduce the number of patients undergoing diagnostic surgery.Additionally, the utilization of intact tissues/FNABs without any preparations is highly advantageous for preclinical examinations and allows further evaluation of the samples after spectroscopic assessments.

F I G U R E 1 1
Abbreviation: NR, not relevant to this study.

F I G U R E 3
The bipartite network (A) and the bipartite network projection (B) of increased metabolites in thyroid lesions versus normal.The network was drown based on the Table

F I G U R E 4
Correlation diagram of increased metabolites in thyroid lesions versus normal.

F I G U R E 5
Increased metabolites (A) and decreased metabolites (B) in malignant vs. benign specimens according a qualitative votecounting results.Each metabolite received one vote per article.The correlation between increased (C) and decreased (D) metabolites in malignant vs. benign specimens.

F I G U R E 6
Forest plots of meta-analysis of lactate, alanine, and citrate on the correlation coefficients obtained from the OPLS-DA models.